Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations740
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)3.5%
Total size in memory121.5 KiB
Average record size in memory168.2 B

Variable types

Numeric14
Categorical7

Alerts

Dataset has 26 (3.5%) duplicate rowsDuplicates
Age is highly overall correlated with Service time and 2 other fieldsHigh correlation
Body mass index is highly overall correlated with Education and 4 other fieldsHigh correlation
Disciplinary failure is highly overall correlated with Reason for absenceHigh correlation
Distance from Residence to Work is highly overall correlated with Social drinker and 1 other fieldsHigh correlation
Education is highly overall correlated with Body mass index and 1 other fieldsHigh correlation
Height is highly overall correlated with Social smokerHigh correlation
ID is highly overall correlated with Education and 3 other fieldsHigh correlation
Month of absence is highly overall correlated with SeasonsHigh correlation
Reason for absence is highly overall correlated with Disciplinary failureHigh correlation
Seasons is highly overall correlated with Month of absenceHigh correlation
Service time is highly overall correlated with Age and 4 other fieldsHigh correlation
Social drinker is highly overall correlated with Age and 6 other fieldsHigh correlation
Social smoker is highly overall correlated with Body mass index and 3 other fieldsHigh correlation
Son is highly overall correlated with Age and 4 other fieldsHigh correlation
Transportation expense is highly overall correlated with Social drinker and 1 other fieldsHigh correlation
Weight is highly overall correlated with Body mass index and 3 other fieldsHigh correlation
Disciplinary failure is highly imbalanced (69.7%) Imbalance
Education is highly imbalanced (56.9%) Imbalance
Social smoker is highly imbalanced (62.3%) Imbalance
Reason for absence has 43 (5.8%) zeros Zeros
Pet has 460 (62.2%) zeros Zeros
Absenteeism time in hours has 44 (5.9%) zeros Zeros

Reproduction

Analysis started2025-03-15 09:42:08.316475
Analysis finished2025-03-15 09:42:54.785971
Duration46.47 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.017568
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:54.941111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median18
Q328
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.021247
Coefficient of variation (CV)0.61169452
Kurtosis-1.2518183
Mean18.017568
Median Absolute Deviation (MAD)10
Skewness0.016605907
Sum13333
Variance121.46789
MonotonicityNot monotonic
2025-03-15T09:42:55.247118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3 113
15.3%
28 76
 
10.3%
34 55
 
7.4%
22 46
 
6.2%
20 42
 
5.7%
11 40
 
5.4%
15 37
 
5.0%
36 34
 
4.6%
24 30
 
4.1%
14 29
 
3.9%
Other values (26) 238
32.2%
ValueCountFrequency (%)
1 23
 
3.1%
2 6
 
0.8%
3 113
15.3%
4 1
 
0.1%
5 19
 
2.6%
6 8
 
1.1%
7 6
 
0.8%
8 2
 
0.3%
9 8
 
1.1%
10 24
 
3.2%
ValueCountFrequency (%)
36 34
4.6%
35 1
 
0.1%
34 55
7.4%
33 24
 
3.2%
32 5
 
0.7%
31 3
 
0.4%
30 7
 
0.9%
29 5
 
0.7%
28 76
10.3%
27 7
 
0.9%

Reason for absence
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.216216
Minimum0
Maximum28
Zeros43
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:55.509964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median23
Q326
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.4334059
Coefficient of variation (CV)0.43886922
Kurtosis-0.25992507
Mean19.216216
Median Absolute Deviation (MAD)5
Skewness-0.91531237
Sum14220
Variance71.122335
MonotonicityNot monotonic
2025-03-15T09:42:55.852918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
23 149
20.1%
28 112
15.1%
27 69
9.3%
13 55
 
7.4%
0 43
 
5.8%
19 40
 
5.4%
22 38
 
5.1%
26 33
 
4.5%
25 31
 
4.2%
11 26
 
3.5%
Other values (18) 144
19.5%
ValueCountFrequency (%)
0 43
5.8%
1 16
 
2.2%
2 1
 
0.1%
3 1
 
0.1%
4 2
 
0.3%
5 3
 
0.4%
6 8
 
1.1%
7 15
 
2.0%
8 6
 
0.8%
9 4
 
0.5%
ValueCountFrequency (%)
28 112
15.1%
27 69
9.3%
26 33
 
4.5%
25 31
 
4.2%
24 3
 
0.4%
23 149
20.1%
22 38
 
5.1%
21 6
 
0.8%
19 40
 
5.4%
18 21
 
2.8%

Month of absence
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3243243
Minimum0
Maximum12
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:56.131705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4362869
Coefficient of variation (CV)0.54334451
Kurtosis-1.2549665
Mean6.3243243
Median Absolute Deviation (MAD)3
Skewness0.069368542
Sum4680
Variance11.808068
MonotonicityNot monotonic
2025-03-15T09:42:56.384508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 87
11.8%
2 72
9.7%
10 71
9.6%
7 67
9.1%
5 64
8.6%
11 63
8.5%
8 54
7.3%
6 54
7.3%
9 53
7.2%
4 53
7.2%
Other values (3) 102
13.8%
ValueCountFrequency (%)
0 3
 
0.4%
1 50
6.8%
2 72
9.7%
3 87
11.8%
4 53
7.2%
5 64
8.6%
6 54
7.3%
7 67
9.1%
8 54
7.3%
9 53
7.2%
ValueCountFrequency (%)
12 49
6.6%
11 63
8.5%
10 71
9.6%
9 53
7.2%
8 54
7.3%
7 67
9.1%
6 54
7.3%
5 64
8.6%
4 53
7.2%
3 87
11.8%

Day of the week
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
2
161 
4
156 
3
154 
6
144 
5
125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Length

2025-03-15T09:42:56.633843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:42:56.824216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Most occurring characters

ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 161
21.8%
4 156
21.1%
3 154
20.8%
6 144
19.5%
5 125
16.9%

Seasons
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
4
195 
2
192 
3
183 
1
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Length

2025-03-15T09:42:57.024410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:42:57.140063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Most occurring characters

ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 195
26.4%
2 192
25.9%
3 183
24.7%
1 170
23.0%

Transportation expense
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.32973
Minimum118
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:57.286341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile118
Q1179
median225
Q3260
95-th percentile361
Maximum388
Range270
Interquartile range (IQR)81

Descriptive statistics

Standard deviation66.952223
Coefficient of variation (CV)0.30249991
Kurtosis-0.31829102
Mean221.32973
Median Absolute Deviation (MAD)46
Skewness0.39618864
Sum163784
Variance4482.6002
MonotonicityNot monotonic
2025-03-15T09:42:57.449226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
179 180
24.3%
118 92
12.4%
225 81
10.9%
235 58
 
7.8%
289 45
 
6.1%
260 42
 
5.7%
291 40
 
5.4%
246 30
 
4.1%
155 29
 
3.9%
361 24
 
3.2%
Other values (14) 119
16.1%
ValueCountFrequency (%)
118 92
12.4%
155 29
 
3.9%
157 7
 
0.9%
179 180
24.3%
184 7
 
0.9%
189 8
 
1.1%
225 81
10.9%
228 8
 
1.1%
231 2
 
0.3%
233 7
 
0.9%
ValueCountFrequency (%)
388 3
 
0.4%
378 8
 
1.1%
369 15
 
2.0%
361 24
3.2%
330 16
 
2.2%
300 5
 
0.7%
291 40
5.4%
289 45
6.1%
279 6
 
0.8%
268 3
 
0.4%

Distance from Residence to Work
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.631081
Minimum5
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:57.605150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q116
median26
Q350
95-th percentile51
Maximum52
Range47
Interquartile range (IQR)34

Descriptive statistics

Standard deviation14.836788
Coefficient of variation (CV)0.50071708
Kurtosis-1.2616826
Mean29.631081
Median Absolute Deviation (MAD)11
Skewness0.31208278
Sum21927
Variance220.13029
MonotonicityNot monotonic
2025-03-15T09:42:57.757576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
26 128
17.3%
51 120
16.2%
10 55
 
7.4%
25 54
 
7.3%
50 45
 
6.1%
36 40
 
5.4%
31 37
 
5.0%
13 34
 
4.6%
12 29
 
3.9%
11 26
 
3.5%
Other values (15) 172
23.2%
ValueCountFrequency (%)
5 6
 
0.8%
10 55
7.4%
11 26
3.5%
12 29
3.9%
13 34
4.6%
14 9
 
1.2%
15 9
 
1.2%
16 26
3.5%
17 15
 
2.0%
20 19
 
2.6%
ValueCountFrequency (%)
52 24
 
3.2%
51 120
16.2%
50 45
 
6.1%
49 8
 
1.1%
48 5
 
0.7%
45 1
 
0.1%
42 7
 
0.9%
36 40
 
5.4%
35 2
 
0.3%
31 37
 
5.0%

Service time
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.554054
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:57.895369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median13
Q316
95-th percentile18
Maximum29
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3848734
Coefficient of variation (CV)0.34927947
Kurtosis0.68311078
Mean12.554054
Median Absolute Deviation (MAD)4
Skewness-0.0047195633
Sum9290
Variance19.227115
MonotonicityNot monotonic
2025-03-15T09:42:58.038969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
18 147
19.9%
9 126
17.0%
14 85
11.5%
13 73
9.9%
12 61
8.2%
10 55
 
7.4%
11 50
 
6.8%
16 38
 
5.1%
3 24
 
3.2%
17 20
 
2.7%
Other values (8) 61
8.2%
ValueCountFrequency (%)
1 7
 
0.9%
3 24
 
3.2%
4 16
 
2.2%
6 7
 
0.9%
7 7
 
0.9%
8 13
 
1.8%
9 126
17.0%
10 55
7.4%
11 50
 
6.8%
12 61
8.2%
ValueCountFrequency (%)
29 5
 
0.7%
24 2
 
0.3%
18 147
19.9%
17 20
 
2.7%
16 38
 
5.1%
15 4
 
0.5%
14 85
11.5%
13 73
9.9%
12 61
8.2%
11 50
 
6.8%

Age
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.45
Minimum27
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:58.179261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28
Q131
median37
Q340
95-th percentile50
Maximum58
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.4787725
Coefficient of variation (CV)0.1777441
Kurtosis0.43161305
Mean36.45
Median Absolute Deviation (MAD)4
Skewness0.69770341
Sum26973
Variance41.974493
MonotonicityNot monotonic
2025-03-15T09:42:58.333593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
28 117
15.8%
38 113
15.3%
37 78
10.5%
40 58
7.8%
33 51
6.9%
36 50
 
6.8%
30 46
 
6.2%
50 37
 
5.0%
41 34
 
4.6%
34 29
 
3.9%
Other values (12) 127
17.2%
ValueCountFrequency (%)
27 7
 
0.9%
28 117
15.8%
29 7
 
0.9%
30 46
 
6.2%
31 22
 
3.0%
32 13
 
1.8%
33 51
6.9%
34 29
 
3.9%
36 50
6.8%
37 78
10.5%
ValueCountFrequency (%)
58 8
 
1.1%
53 1
 
0.1%
50 37
5.0%
49 5
 
0.7%
48 6
 
0.8%
47 24
3.2%
46 2
 
0.3%
43 24
3.2%
41 34
4.6%
40 58
7.8%

Work load Average/day
Real number (ℝ)

Distinct38
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.49024
Minimum205.917
Maximum378.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:58.959416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum205.917
5-th percentile222.196
Q1244.387
median264.249
Q3294.217
95-th percentile343.253
Maximum378.884
Range172.967
Interquartile range (IQR)49.83

Descriptive statistics

Standard deviation39.058116
Coefficient of variation (CV)0.14386564
Kurtosis0.61818796
Mean271.49024
Median Absolute Deviation (MAD)20.604
Skewness0.96145661
Sum200902.77
Variance1525.5364
MonotonicityNot monotonic
2025-03-15T09:42:59.136564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
222.196 36
 
4.9%
264.249 33
 
4.5%
237.656 32
 
4.3%
343.253 29
 
3.9%
265.017 28
 
3.8%
284.853 25
 
3.4%
308.593 24
 
3.2%
268.519 23
 
3.1%
244.387 22
 
3.0%
241.476 22
 
3.0%
Other values (28) 466
63.0%
ValueCountFrequency (%)
205.917 21
2.8%
222.196 36
4.9%
230.29 20
2.7%
236.629 19
2.6%
237.656 32
4.3%
239.409 13
 
1.8%
239.554 19
2.6%
241.476 22
3.0%
244.387 22
3.0%
246.074 16
2.2%
ValueCountFrequency (%)
378.884 16
2.2%
377.55 16
2.2%
343.253 29
3.9%
330.061 11
 
1.5%
326.452 20
2.7%
313.532 15
2.0%
308.593 24
3.2%
306.345 18
2.4%
302.585 18
2.4%
294.217 19
2.6%

Hit target
Real number (ℝ)

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.587838
Minimum81
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:42:59.287092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile88
Q193
median95
Q397
95-th percentile99
Maximum100
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7793131
Coefficient of variation (CV)0.039955593
Kurtosis2.4190423
Mean94.587838
Median Absolute Deviation (MAD)2
Skewness-1.2617082
Sum69995
Variance14.283208
MonotonicityNot monotonic
2025-03-15T09:42:59.451140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
93 105
14.2%
99 102
13.8%
97 89
12.0%
92 79
10.7%
95 75
10.1%
96 75
10.1%
98 66
8.9%
91 45
6.1%
94 34
 
4.6%
88 28
 
3.8%
Other values (3) 42
 
5.7%
ValueCountFrequency (%)
81 19
 
2.6%
87 12
 
1.6%
88 28
 
3.8%
91 45
6.1%
92 79
10.7%
93 105
14.2%
94 34
 
4.6%
95 75
10.1%
96 75
10.1%
97 89
12.0%
ValueCountFrequency (%)
100 11
 
1.5%
99 102
13.8%
98 66
8.9%
97 89
12.0%
96 75
10.1%
95 75
10.1%
94 34
 
4.6%
93 105
14.2%
92 79
10.7%
91 45
6.1%

Disciplinary failure
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0
700 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Length

2025-03-15T09:42:59.613760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:42:59.703643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 700
94.6%
1 40
 
5.4%

Education
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
1
611 
3
79 
2
 
46
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Length

2025-03-15T09:42:59.817213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:42:59.922268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 611
82.6%
3 79
 
10.7%
2 46
 
6.2%
4 4
 
0.5%

Son
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0
298 
1
229 
2
156 
4
42 
3
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Length

2025-03-15T09:43:00.056952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:43:00.177355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 298
40.3%
1 229
30.9%
2 156
21.1%
4 42
 
5.7%
3 15
 
2.0%

Social drinker
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
1
420 
0
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Length

2025-03-15T09:43:00.325936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:43:00.445939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Most occurring characters

ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 420
56.8%
0 320
43.2%

Social smoker
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
0
686 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Length

2025-03-15T09:43:00.565674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T09:43:00.663002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 686
92.7%
1 54
 
7.3%

Pet
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74594595
Minimum0
Maximum8
Zeros460
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:43:00.745947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3182583
Coefficient of variation (CV)1.7672303
Kurtosis9.6748269
Mean0.74594595
Median Absolute Deviation (MAD)0
Skewness2.7357154
Sum552
Variance1.7378049
MonotonicityNot monotonic
2025-03-15T09:43:00.875839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 460
62.2%
1 138
 
18.6%
2 96
 
13.0%
4 32
 
4.3%
8 8
 
1.1%
5 6
 
0.8%
ValueCountFrequency (%)
0 460
62.2%
1 138
 
18.6%
2 96
 
13.0%
4 32
 
4.3%
5 6
 
0.8%
8 8
 
1.1%
ValueCountFrequency (%)
8 8
 
1.1%
5 6
 
0.8%
4 32
 
4.3%
2 96
 
13.0%
1 138
 
18.6%
0 460
62.2%

Weight
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.035135
Minimum56
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:43:01.020444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile56
Q169
median83
Q389
95-th percentile98
Maximum108
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.883211
Coefficient of variation (CV)0.16300612
Kurtosis-0.91392762
Mean79.035135
Median Absolute Deviation (MAD)11
Skewness0.017001372
Sum58486
Variance165.97711
MonotonicityNot monotonic
2025-03-15T09:43:01.186682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
89 113
15.3%
69 85
11.5%
65 61
 
8.2%
83 55
 
7.4%
56 46
 
6.2%
90 40
 
5.4%
73 37
 
5.0%
98 35
 
4.7%
67 30
 
4.1%
95 29
 
3.9%
Other values (16) 209
28.2%
ValueCountFrequency (%)
56 46
6.2%
58 7
 
0.9%
63 20
 
2.7%
65 61
8.2%
67 30
 
4.1%
68 13
 
1.8%
69 85
11.5%
70 15
 
2.0%
73 37
5.0%
75 19
 
2.6%
ValueCountFrequency (%)
108 5
 
0.7%
106 19
 
2.6%
100 2
 
0.3%
98 35
 
4.7%
95 29
 
3.9%
94 4
 
0.5%
90 40
 
5.4%
89 113
15.3%
88 29
 
3.9%
86 24
 
3.2%

Height
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.11486
Minimum163
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:43:01.320978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile167
Q1169
median170
Q3172
95-th percentile182
Maximum196
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.0349945
Coefficient of variation (CV)0.035063761
Kurtosis7.3172355
Mean172.11486
Median Absolute Deviation (MAD)2
Skewness2.5660597
Sum127365
Variance36.421159
MonotonicityNot monotonic
2025-03-15T09:43:01.479376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
170 166
22.4%
172 155
20.9%
169 95
12.8%
171 83
11.2%
178 57
 
7.7%
168 48
 
6.5%
167 34
 
4.6%
196 29
 
3.9%
165 24
 
3.2%
182 20
 
2.7%
Other values (4) 29
 
3.9%
ValueCountFrequency (%)
163 6
 
0.8%
165 24
 
3.2%
167 34
 
4.6%
168 48
 
6.5%
169 95
12.8%
170 166
22.4%
171 83
11.2%
172 155
20.9%
174 8
 
1.1%
175 8
 
1.1%
ValueCountFrequency (%)
196 29
 
3.9%
185 7
 
0.9%
182 20
 
2.7%
178 57
 
7.7%
175 8
 
1.1%
174 8
 
1.1%
172 155
20.9%
171 83
11.2%
170 166
22.4%
169 95
12.8%

Body mass index
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.677027
Minimum19
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:43:01.621041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19
Q124
median25
Q331
95-th percentile32
Maximum38
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2854522
Coefficient of variation (CV)0.16064205
Kurtosis-0.314375
Mean26.677027
Median Absolute Deviation (MAD)3
Skewness0.30504566
Sum19741
Variance18.365101
MonotonicityNot monotonic
2025-03-15T09:43:01.767995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
31 147
19.9%
25 126
17.0%
24 86
11.6%
23 75
10.1%
28 59
8.0%
19 46
 
6.2%
30 40
 
5.4%
22 35
 
4.7%
32 24
 
3.2%
27 24
 
3.2%
Other values (7) 78
10.5%
ValueCountFrequency (%)
19 46
 
6.2%
21 22
 
3.0%
22 35
 
4.7%
23 75
10.1%
24 86
11.6%
25 126
17.0%
27 24
 
3.2%
28 59
8.0%
29 23
 
3.1%
30 40
 
5.4%
ValueCountFrequency (%)
38 19
 
2.6%
36 5
 
0.7%
35 2
 
0.3%
34 1
 
0.1%
33 6
 
0.8%
32 24
 
3.2%
31 147
19.9%
30 40
 
5.4%
29 23
 
3.1%
28 59
8.0%

Absenteeism time in hours
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9243243
Minimum0
Maximum120
Zeros44
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2025-03-15T09:43:01.915218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q38
95-th percentile24
Maximum120
Range120
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.330998
Coefficient of variation (CV)1.9252417
Kurtosis38.777307
Mean6.9243243
Median Absolute Deviation (MAD)2
Skewness5.7207279
Sum5124
Variance177.71551
MonotonicityNot monotonic
2025-03-15T09:43:02.062202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8 208
28.1%
2 157
21.2%
3 112
15.1%
1 88
11.9%
4 60
 
8.1%
0 44
 
5.9%
16 19
 
2.6%
24 16
 
2.2%
5 7
 
0.9%
40 7
 
0.9%
Other values (9) 22
 
3.0%
ValueCountFrequency (%)
0 44
 
5.9%
1 88
11.9%
2 157
21.2%
3 112
15.1%
4 60
 
8.1%
5 7
 
0.9%
7 1
 
0.1%
8 208
28.1%
16 19
 
2.6%
24 16
 
2.2%
ValueCountFrequency (%)
120 3
 
0.4%
112 2
 
0.3%
104 1
 
0.1%
80 3
 
0.4%
64 3
 
0.4%
56 2
 
0.3%
48 1
 
0.1%
40 7
0.9%
32 6
 
0.8%
24 16
2.2%

Interactions

2025-03-15T09:42:51.541423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:09.934022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:14.035397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:19.393392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:24.018500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.588065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.138129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.831700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.049553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:38.667399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:41.040835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.416328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.559653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.177449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:51.695992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:10.211915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:14.380628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:19.749490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:24.382498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.734832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.289436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.977547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.220029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:38.861503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:41.277219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.571970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.710536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.335948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:51.859984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:10.373407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:14.637652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:19.999437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:24.722497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.970285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.443554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.134900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.387192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.016743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:41.527709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.738579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.888612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.500218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.044124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:10.559006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:14.967611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:20.289109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:25.202620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:28.216452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.591973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.319039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.557189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.192417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:41.751683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.893705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:47.046060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.658349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.206380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:10.852703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:15.635488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:20.567837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:25.468057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:28.498816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.739765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.470215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.708965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.351896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:42.032241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.036782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:47.576305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.845156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.354952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:11.071062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:16.098522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:21.199608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:25.794139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:28.710200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:31.922002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.610369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:36.872782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.527286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:42.312122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.177446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:47.721947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.019009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.512160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:11.633723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:16.604875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:21.579981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:25.953680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:28.968235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:32.104503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.774054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:37.033033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.686704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:42.601108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.325492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:47.902549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.176649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.660433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:11.923812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:17.051426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:21.878828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:26.171198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:29.247057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:32.383338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:34.945613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:37.192195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:39.847448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:42.885750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.463837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.049133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.332001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:52.840868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:12.337981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:17.553298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:22.116544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:26.521393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:29.530323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:32.633423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.103466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:37.356818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.017111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:43.164927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.624085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.210510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.506239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:53.031024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:12.692430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:17.846296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:22.446581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:26.726145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:29.807748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:32.910680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.279871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:37.527051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.179098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:43.432929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.785711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.366735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.674694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:53.210371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:13.015826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:18.153876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:22.705853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:26.949859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:30.059741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.178946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.441423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:37.690107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.342422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:43.734259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:45.954511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.530884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:50.846668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:53.372209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:13.275358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:18.404698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:23.001519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.116599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:30.257057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.326092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.584491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:38.160678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.507880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:43.940106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.097976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.673764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:51.031201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:53.524523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:13.520965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:18.789660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:23.367208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.271212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:30.795049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.501651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.736158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:38.329083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.662946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.100984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.254608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:48.835753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:51.192769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:53.688678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:13.742356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:19.092544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:23.743850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:27.434604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:30.972130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:33.658295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:35.898291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:38.514590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:40.828142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:44.262289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:46.404858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:49.017066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T09:42:51.366146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-15T09:43:02.223369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Absenteeism time in hoursAgeBody mass indexDay of the weekDisciplinary failureDistance from Residence to WorkEducationHeightHit targetIDMonth of absencePetReason for absenceSeasonsService timeSocial drinkerSocial smokerSonTransportation expenseWeightWork load Average/day
Absenteeism time in hours1.000-0.070-0.0630.0850.0000.0100.0000.0710.047-0.1300.0100.002-0.2220.000-0.0300.0910.0000.0770.166-0.0090.012
Age-0.0701.0000.4800.1000.171-0.1480.262-0.019-0.013-0.0590.002-0.2760.0210.0810.7590.5720.2870.580-0.1410.369-0.062
Body mass index-0.0630.4801.0000.0740.1790.0800.5670.009-0.046-0.2980.032-0.0310.0670.1090.5650.5040.6740.446-0.1480.887-0.106
Day of the week0.0850.1000.0741.0000.0000.0860.0000.0000.0000.0970.0000.0130.0770.0420.0670.0000.0000.1300.1240.1060.010
Disciplinary failure0.0000.1710.1790.0001.0000.1240.0190.1810.1770.1630.1870.0970.7980.1640.0700.0270.0990.0830.1930.1840.080
Distance from Residence to Work0.010-0.1480.0800.0860.1241.0000.391-0.318-0.010-0.4960.0010.1970.1610.0910.0970.6000.5210.4780.287-0.042-0.077
Education0.0000.2620.5670.0000.0190.3911.0000.4280.0800.5840.0760.2580.1120.0540.4480.4680.3590.1740.4230.4850.061
Height0.071-0.0190.0090.0000.181-0.3180.4281.0000.0710.110-0.042-0.084-0.1160.129-0.0410.3410.6170.452-0.1980.3230.010
Hit target0.047-0.013-0.0460.0000.177-0.0100.0800.0711.000-0.014-0.475-0.0500.0550.4340.0380.1590.0000.064-0.097-0.018-0.128
ID-0.130-0.059-0.2980.0970.163-0.4960.5840.110-0.0141.000-0.0010.014-0.0500.108-0.3740.6240.5360.552-0.215-0.2690.111
Month of absence0.0100.0020.0320.0000.1870.0010.076-0.042-0.475-0.0011.0000.086-0.0830.889-0.0620.1420.1260.1100.1540.011-0.087
Pet0.002-0.276-0.0310.0130.0970.1970.258-0.084-0.0500.0140.0861.000-0.0980.108-0.3420.4140.3620.3960.471-0.0790.051
Reason for absence-0.2220.0210.0670.0770.7980.1610.112-0.1160.055-0.050-0.083-0.0981.0000.1500.0840.2150.2240.191-0.1220.017-0.129
Seasons0.0000.0810.1090.0420.1640.0910.0540.1290.4340.1080.8890.1080.1501.0000.1150.0600.0710.1260.1570.1420.449
Service time-0.0300.7590.5650.0670.0700.0970.448-0.0410.038-0.374-0.062-0.3420.0840.1151.0000.6780.4120.505-0.2850.535-0.072
Social drinker0.0910.5720.5040.0000.0270.6000.4680.3410.1590.6240.1420.4140.2150.0600.6781.0000.1000.2440.5760.6760.076
Social smoker0.0000.2870.6740.0000.0990.5210.3590.6170.0000.5360.1260.3620.2240.0710.4120.1001.0000.3710.2290.3930.102
Son0.0770.5800.4460.1300.0830.4780.1740.4520.0640.5520.1100.3960.1910.1260.5050.2440.3711.0000.7250.5650.074
Transportation expense0.166-0.141-0.1480.1240.1930.2870.423-0.198-0.097-0.2150.1540.471-0.1220.157-0.2850.5760.2290.7251.000-0.2190.003
Weight-0.0090.3690.8870.1060.184-0.0420.4850.323-0.018-0.2690.011-0.0790.0170.1420.5350.6760.3930.565-0.2191.000-0.049
Work load Average/day0.012-0.062-0.1060.0100.080-0.0770.0610.010-0.1280.111-0.0870.051-0.1290.449-0.0720.0760.1020.0740.003-0.0491.000

Missing values

2025-03-15T09:42:54.092582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-15T09:42:54.491191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
01126731289361333239.5549701210190172304
1360731118131850239.5549711110098178310
2323741179511838239.5549701010089170312
37775127951439239.5549701211068168244
41123751289361333239.5549701210190172302
5323761179511838239.5549701010089170312
6102276136152328239.5549701110480172278
72023761260501136239.5549701410065168234
81419721155121434239.55497012100951962540
9122721235111437239.5549703100188172298
IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
730622731189291333264.60493012002691672516
7313423741118101037264.6049301000083172282
732102274136152328264.6049301110480172278
733282274122526928264.6049301100269169248
7341313721369171231264.60493013100701692580
7351114731289361333264.6049301210190172308
736111731235111437264.6049303100188172294
73740031118141340271.2199501110898170340
73880042231351439271.21995012102100170350
739350063179451453271.2199501100177175250

Duplicate rows

Most frequently occurring

IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours# duplicates
2327242179511838251.81896010100891703134
14222746317926930246.28891030000561711924
3327242179511838264.24997010100891703123
5327262179511838251.81896010100891703133
7327342179511838222.19699010100891703123
8327352179511838222.19699010100891703133
0323761179511838239.55497010100891703122
1327222179511838264.24997010100891703122
4327252179511838264.24997010100891703122
6327262179511838264.24997010100891703122